--- base_model: toobiza/MT-ancient-spaceship-83 tags: - generated_from_trainer model-index: - name: MT-legendary-capybara-96 results: [] --- # MT-legendary-capybara-96 This model is a fine-tuned version of [toobiza/MT-ancient-spaceship-83](https://huggingface.co/toobiza/MT-ancient-spaceship-83) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1572 - Loss Ce: 0.0000 - Loss Bbox: 0.0216 - Cardinality Error: 1.0 - Giou: 97.5514 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Loss Ce | Loss Bbox | Cardinality Error | Giou | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-----------------:|:-------:| | 0.2851 | 0.24 | 200 | 0.1903 | 0.0000 | 0.0263 | 1.0 | 97.0566 | | 0.1809 | 0.48 | 400 | 0.1726 | 0.0000 | 0.0237 | 1.0 | 97.2974 | | 0.1909 | 0.73 | 600 | 0.1923 | 0.0000 | 0.0268 | 1.0 | 97.0772 | | 0.1808 | 0.97 | 800 | 0.1745 | 0.0000 | 0.0239 | 1.0 | 97.2598 | | 0.169 | 1.21 | 1000 | 0.1774 | 0.0000 | 0.0245 | 1.0 | 97.2469 | | 0.1916 | 1.45 | 1200 | 0.1800 | 0.0000 | 0.0249 | 1.0 | 97.2128 | | 0.1511 | 1.69 | 1400 | 0.1810 | 0.0000 | 0.0251 | 1.0 | 97.2199 | | 0.1205 | 1.93 | 1600 | 0.1811 | 0.0000 | 0.0251 | 1.0 | 97.2107 | | 0.0905 | 2.18 | 1800 | 0.1816 | 0.0000 | 0.0252 | 1.0 | 97.2090 | | 0.1175 | 2.42 | 2000 | 0.1789 | 0.0000 | 0.0247 | 1.0 | 97.2187 | | 0.1781 | 2.66 | 2200 | 0.1713 | 0.0000 | 0.0236 | 1.0 | 97.3242 | | 0.1751 | 2.9 | 2400 | 0.1886 | 0.0000 | 0.0261 | 1.0 | 97.0914 | | 0.1084 | 3.14 | 2600 | 0.1692 | 0.0000 | 0.0232 | 1.0 | 97.3369 | | 0.1171 | 3.39 | 2800 | 0.1570 | 0.0000 | 0.0216 | 1.0 | 97.5552 | | 0.1191 | 3.63 | 3000 | 0.1859 | 0.0000 | 0.0259 | 1.0 | 97.1879 | | 0.1515 | 3.87 | 3200 | 0.1598 | 0.0000 | 0.0221 | 1.0 | 97.5370 | | 0.1529 | 4.11 | 3400 | 0.1750 | 0.0000 | 0.0240 | 1.0 | 97.2571 | | 0.1169 | 4.35 | 3600 | 0.1627 | 0.0000 | 0.0224 | 1.0 | 97.4536 | | 0.1433 | 4.59 | 3800 | 0.1764 | 0.0000 | 0.0244 | 1.0 | 97.2739 | | 0.0873 | 4.84 | 4000 | 0.1536 | 0.0000 | 0.0209 | 1.0 | 97.5448 | | 0.1176 | 5.08 | 4200 | 0.1545 | 0.0000 | 0.0212 | 1.0 | 97.5786 | | 0.0921 | 5.32 | 4400 | 0.1580 | 0.0000 | 0.0216 | 1.0 | 97.5027 | | 0.0894 | 5.56 | 4600 | 0.1579 | 0.0000 | 0.0216 | 1.0 | 97.5178 | | 0.0843 | 5.8 | 4800 | 0.1604 | 0.0000 | 0.0220 | 1.0 | 97.4857 | | 0.1446 | 6.05 | 5000 | 0.1692 | 0.0000 | 0.0233 | 1.0 | 97.3695 | | 0.0929 | 6.29 | 5200 | 0.1723 | 0.0000 | 0.0238 | 1.0 | 97.3369 | | 0.0831 | 6.53 | 5400 | 0.1638 | 0.0000 | 0.0225 | 1.0 | 97.4370 | | 0.093 | 6.77 | 5600 | 0.1606 | 0.0000 | 0.0220 | 1.0 | 97.4782 | | 0.0869 | 7.01 | 5800 | 0.1604 | 0.0000 | 0.0220 | 1.0 | 97.4893 | | 0.1183 | 7.26 | 6000 | 0.1599 | 0.0000 | 0.0219 | 1.0 | 97.4886 | | 0.0807 | 7.5 | 6200 | 0.1614 | 0.0000 | 0.0222 | 1.0 | 97.4926 | | 0.0851 | 7.74 | 6400 | 0.1642 | 0.0000 | 0.0226 | 1.0 | 97.4411 | | 0.1279 | 7.98 | 6600 | 0.1596 | 0.0000 | 0.0220 | 1.0 | 97.5193 | | 0.0828 | 8.22 | 6800 | 0.1606 | 0.0000 | 0.0222 | 1.0 | 97.5183 | | 0.0933 | 8.46 | 7000 | 0.1576 | 0.0000 | 0.0217 | 1.0 | 97.5506 | | 0.085 | 8.71 | 7200 | 0.1584 | 0.0000 | 0.0218 | 1.0 | 97.5329 | | 0.0736 | 8.95 | 7400 | 0.1564 | 0.0000 | 0.0215 | 1.0 | 97.5616 | | 0.1001 | 9.19 | 7600 | 0.1581 | 0.0000 | 0.0217 | 1.0 | 97.5258 | | 0.075 | 9.43 | 7800 | 0.1575 | 0.0000 | 0.0217 | 1.0 | 97.5435 | | 0.0714 | 9.67 | 8000 | 0.1571 | 0.0000 | 0.0216 | 1.0 | 97.5487 | | 0.0881 | 9.92 | 8200 | 0.1572 | 0.0000 | 0.0216 | 1.0 | 97.5514 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.13.3